章节 1：诺贝尔奖效应与将“科学方法”引入商业

📝 本节摘要：

访谈以 Mike Allen 的开场介绍开始，祝贺 Demis Hassabis 近期获得诺贝尔奖。Demis 分享了获奖后的生活变化，指出诺贝尔奖成为了与非AI领域人士（如政府官员）沟通的有效“通行证”。随后，他阐述了 DeepMind 的核心文化：将“科学方法”（观察、假设、实验、更新）不仅应用于研究，更作为一种管理哲学应用于商业决策和工程开发中，这被视为他们在激烈竞争中保持优势的关键。

Start of Content

[原文] [Mike Allen]: thank you very much big finish i'm Mike Allen co-founder of Axio on bealf of my co-founders Roy Schwarz Jim Vanhey thank you to all of you who for coming up nine years now have been fans of Axios and thank you for turning out here in San Francisco in this historic bank this very cool uh setting for this Axios AI plus SF summit uh welcome to all of you around the world for our big finish deis Hassabis PhD co-founder and CEO of Google Deep Mind he's a neuroscientist and entrepreneur and AI pioneer demis was a chess prodigy at five a Nobel laureate at 48 he's a Britishborn genius he's been kned deisabas welcome to Axios

[译文] [Mike Allen]: 非常感谢大家，我们将迎来压轴环节。我是 Axios 的联合创始人 Mike Allen，代表我的联合创始人 Roy Schwarz 和 Jim VandeHei，感谢大家九年来一直支持 Axios。感谢大家来到旧金山这座历史悠久的银行建筑，在这个非常酷的场地参加 Axios AI+ 旧金山峰会。欢迎世界各地的观众收看我们的压轴访谈。Demis Hassabis 博士是 Google DeepMind 的联合创始人兼 CEO，他是一位神经科学家、企业家和 AI 先驱。Demis 5岁时就是国际象棋神童，48岁成为诺贝尔奖得主，他是一位出生于英国的天才，并已被封为爵士。Demis Hassabis，欢迎来到 Axios。

[原文] [Demis Hassabis]: thank you so much thanks for having me we've been looking forward to this we appreciate to be here

[译文] [Demis Hassabis]: 非常感谢，谢谢邀请。我们一直很期待这次活动，很高兴能来到这里。

[原文] [Mike Allen]: it was just over 400 days ago that you found out you were a Nobel laureate and you said in that moment you said this is surreal this is the big one what has changed since then about your life and work what has it made possible um

[译文] [Mike Allen]: 就在400多天前（注：原文口误，应为近期），你得知自己成为了诺贝尔奖得主。你在那一刻说：“这太超现实了，这是那个最大的奖项。”从那以后，你的生活和工作发生了什么变化？它让什么成为了可能？

[原文] [Demis Hassabis]: well look it's still pretty surreal actually s till hasn't fully sunk in but uh it has made quite a big difference the the thing it makes a difference to is when you speak to people not in your field including you know big government people things like that who maybe don't know that much about AI if you you know you have the Nobel Prize it's a sort of shortcut to almost anyone to to know that you're you know you're expert in your field so it's it's going to be useful I think in the future

[译文] [Demis Hassabis]: 嗯，这仍然感觉很超现实，实际上我还没完全缓过神来。但这确实带来了很大的不同。最大的不同在于当你与非本领域的人交谈时——比如政府高层之类的人，他们可能对 AI 了解不多——如果你拥有诺贝尔奖，这就像是一种捷径，能让几乎任何人立刻知道你是该领域的专家。所以我认为这在未来会很有用。

[原文] [Mike Allen]: and and you had endless resources at your disposal are there new resources that you have or that you think you can tap now

[译文] [Mike Allen]: 你之前已经拥有了无尽的资源供你支配，现在是否有新的资源是你拥有或认为可以利用的？

[原文] [Demis Hassabis]: not really i mean you're right we we're lucky at at Google at Deep Mind we have we have a lot of resources they're not endless we always need more more compute uh no matter how much compute you have but um but we have you know a lot of great things which is why we're able to do such a broad portfolio of things but it's mostly again this uh this platform it gives you to basically speak out about things that you care about and I haven't done a lot of that yet but I think it will be important maybe we're going to talk about AI safety and other things i think uh the Nobel and the platform that gives you uh could be useful for that

[译文] [Demis Hassabis]: 其实并没有。你是对的，我们在 Google DeepMind 很幸运，拥有很多资源，虽然不是无尽的——无论你有多少算力，我们总是需要更多算力——但我们确实拥有很多很棒的条件，这就是为什么我们能开展如此广泛的项目组合。但这（诺贝尔奖）主要还是给了你一个平台，让你能为你关心的事情发声。我还没怎么这么做，但我认为这很重要。也许我们会讨论 AI 安全和其他议题，我认为诺贝尔奖及其赋予的平台对此会很有用。

[原文] [Mike Allen]: and what's on the short list of in addition to AI safety that you think you'll be talking more about using your platform

[译文] [Mike Allen]: 除了 AI 安全，还有哪些议题在你的清单上，你会利用这个平台更多地讨论？

[原文] [Demis Hassabis]: yeah well it's not just about uh safety in the long term agi safety obviously I think we think a lot about that but it's also about responsible use of AI today uh what are the kinds of things we should be using AI to to improve and to to power up and to accelerate and maybe you know what sorts of things we should be careful about um uh uh in the even in the near term so I think that's one thing I think also just getting society ready for what's to come you know AGI probably the most transformative moment in human history is on the horizon and um we need to get prepared as a society um and as a species and I think of course governments and other important people uh uh other important leaders are going to be critical in that and I think having having something like the Nobel platform opens pretty much any door

[译文] [Demis Hassabis]: 没错，这不仅仅关于长期的安全性——显然我们会深入思考通用人工智能（AGI）的安全性——但这同时也关于今天如何负责任地使用 AI。我们应该用 AI 来改善、赋能和加速哪些事物？又有哪些事物即便在短期内我们也应该保持谨慎？我认为这是一方面。另一方面是让社会为即将到来的事情做好准备。你知道，AGI 很可能是人类历史上最具变革性的时刻，它即将到来，我们需要作为一个社会、作为一个物种做好准备。我认为政府和其他重要人物、重要领导者在其中将起到关键作用，而拥有像诺贝尔奖这样的平台几乎可以敲开任何一扇门。

[原文] [Mike Allen]: one of the things that distinguishes you is you're deep in the science and yet you also are on the front line of this fight and this race among companies hyperscalers superpowers and you sort of in the mold of Steve Jobs you also have a product mind you want to create delightful things for people but you always say you're a scientist first

[译文] [Mike Allen]: 让你与众不同的一点是，你既深耕科学，又处于公司、超大规模云计算巨头（hyperscalers）和超级大国之间这场战斗和竞赛的最前线。而且你有点像史蒂夫·乔布斯，拥有产品思维，想要为人们创造令人愉悦的东西，但你总是说你首先是一名科学家。

[原文] [Demis Hassabis]: yeah science i'm a scientist first the reason I say that is that's the that's the sort of default approach I take to everything so um and what I mean by that is the scientific method really that way of thinking um I really love the I mean I think it's the most the scientific method is is the most important maybe idea humanity's ever had um you know created the enlightenment and then modern science so basically modern civilization depends on on on this on this idea of scientific method and experimentation and then updating your hypothesis and so on and I think it's an incredibly powerful method but I think it can be applied to more than just science i think it can be applied to everyday living and indeed business um and that's what I've tried to do is sort of take that uh uh to its limit and I think that's what gives us um you know advantage in some ways as a as a research organization as an engineering organization yes we're in the middle of this ferocious probably the most ferocious competitive battle maybe tech has ever seen um and uh but one of the things that I think gives us an edge is is the rigor and precision we bring to our work um because um we have a scientific method sort of at the heart of it and we blend world-class research with world-class engineering with world-class infrastructure and I think you need all three of those things to be at the frontier of something like AI and I think you know we we're we're sort of pretty unique in having uh worldclass capabilities in in all those areas um

[译文] [Demis Hassabis]: 是的，我首先是一名科学家。我之所以这么说，是因为那是我处理所有事情的默认方式。我指的是“科学方法”，那种思维方式。我真的热爱它，我认为科学方法可能是人类产生的最重要的思想。它创造了启蒙运动和现代科学，基本上现代文明都依赖于科学方法、实验、更新假设等这一整套理念。我认为这是一种极其强大的方法，但它不仅可以应用于科学，还可以应用于日常生活，甚至商业。这正是我试图做的——将其发挥到极致。我认为这在某种程度上赋予了我们作为研究机构和工程机构的优势。是的，我们正处于一场激烈的、可能是科技界有史以来最激烈的竞争战役中，但我认为让我们具备优势的一点是我们带入工作中的严谨性和精确性，因为我们的核心是科学方法。我们将世界级的研究、世界级的工程和世界级的基础设施融合在一起。我认为要在 AI 这样的前沿领域立足，这三者缺一不可。而在所有这些领域都拥有世界级能力方面，我们是相当独特的。

End of Content

章节 2：未来12个月的预测——多模态与智能体（Agents）

📝 本节摘要：

Mike Allen 开启“宏观视角（Zoom Out）”环节，要求 Demis 毫无炒作地预测未来一年的 AI 进展。Demis 指出两个核心趋势：一是多模态融合（视频、音频、文本的交互与生成），特别是 Gemini 和新的图像模型；二是世界模型与智能体（Agents）。他预测虽然目前的智能体还不足以完全托管任务，但一年后将出现能够可靠执行任务的“通用助手”雏形。

Start of Content

[原文] [Mike Allen]: yeah so in Axio fashion we're going to divide our conversation between zoom out and zoom in so zoom out uh getting your priceless uh mind on the state of AI so we're going to talk about the blunt state of AI and what I'm going to ask from you is given the known knowns today be blunt clinical no hype no soft selling can we do that i'll do my best all right um what does the next 12 months of progress look like what do you believe that if if we sit here a year from today and I would love to uh what will have changed in the world

[译文] [Mike Allen]: 好的，按照 Axios 的风格，我们将对话分为“宏观视角（zoom out）”和“微观视角（zoom in）”。首先是宏观视角，让我们听听你对 AI 现状的宝贵见解。我们要谈论 AI 最真实的现状，我要求你基于今天已知的确切事实，直言不讳、客观冷静，不要炒作，不要软性推销。能做到吗？（Demis: 我会尽力。）好的。未来12个月的进展会是什么样？如果你相信——如果我们一年后还坐在这里（我很乐意这样做）——这个世界将会发生什么变化？

[原文] [Demis Hassabis]: um I think the things that that we're we're pressing hard on are um uh the convergence of modalities so you Gemini which is our main foundation model has always been multimodal from the beginning it takes images video uh text audio and then can produce now increasingly produce those uh uh types of outputs as well um and I think we're getting some really interesting uh cross-pollination by being multimodal one the best example of that is our latest image model NO Banana Pro which um I think shows some astonishing sort of understanding of visuals and it can kind of you know create infographics that are really accurate and so on so I think over the next year you're going to see that uh uh progress a lot and I think for example in video when that converges with the language models you're going to be see some very interesting combinations of capabilities there i think the other things we're going to see over the next year and I'm personally working on is world models so uh we have this um uh uh system called Genie Genie 3 which is like an interactive video model you can think about so you can sort of generate a video but then you can start walking around it like you're in a game or simulation and it stays coherent for a minute i think that's very exciting

[译文] [Demis Hassabis]: 嗯，我认为我们正在大力推进的是模态的融合。Gemini 是我们的主要基础模型，它从一开始就是多模态的，可以接收图像、视频、文本、音频，现在也越来越能生成这些类型的输出。我认为通过多模态化，我们正在获得一些非常有趣的交叉融合效应。最好的例子是我们最新的图像模型 NO Banana Pro（注：可能指Imagen 3或类似内部代号，原文转录如此），它展现了对视觉内容惊人的理解力，能制作非常准确的信息图表等。所以我认为在接下来的一年里，你会看到这方面有很大进展。例如在视频领域，当它与语言模型融合时，你会看到一些非常有趣的能力组合。我认为明年还会看到的另一件事——也是我个人正在研究的——是世界模型（World Models）。我们有一个叫 Genie（Genie 3）的系统，你可以把它想象成一个交互式视频模型。你可以生成一个视频，然后像在游戏或模拟中一样在里面走动，并且它能保持大约一分钟的连贯性。我认为这非常令人兴奋。

[原文] [Demis Hassabis]: um and then uh you know maybe the other thing is a agent based systems so we I think the field's been talking a lot about agents but then they're not reliable yet enough to do full tasks but I think over the next

[译文] [Demis Hassabis]: 然后，也许另一件事是基于智能体（Agent-based）的系统。我认为业界一直在谈论智能体，但它们目前还不够可靠，无法完成完整的任务。但我认为在接下来...

[原文] [Mike Allen]: we've heard a lot about that today here on the Axia stage what would you say a year from now how will agents have progressed what's an example of how it will work in everyday life a year from now

[译文] [Mike Allen]: 我们今天在 Axios 的舞台上听到了很多关于这方面的内容。你会怎么说，一年后智能体会有怎样的进展？举个例子说明一年后它在日常生活中会如何运作？

[原文] [Demis Hassabis]: well look I we we have this concept of a universal assistant that we want Gemini eventually to become uh I think this is also you're going to see from us over the next year this will be on on on more devices as well by universal we mean it's not just on your computer or your laptop or your or your phone but maybe comes around with you on glasses or other devices and um I think it needs you know we want to create something that is useful to you in your everyday life that you consult many times a day it becomes a part of the fabric of your life and it just improves your productivity but also your personal life you know recommendations for books and films and other or activities that you'd like and but yeah so but agents at the moment they can't comp you can't delegate to them uh a whole task and be sure they're going to complete that entire task uh uh completely reliably but a year from now you think they will I think a year from now we'll start having agents that uh are close uh to doing that

[译文] [Demis Hassabis]: 嗯，我们有一个“通用助手（Universal Assistant）”的概念，这是我们希望 Gemini 最终成为的样子。我认为接下来一年你也会看到它出现在更多设备上。所谓“通用”，是指它不仅存在于你的电脑、笔记本或手机上，可能还会通过眼镜或其他设备伴随你。我们想创造一种在你日常生活中非常有用的东西，你每天会咨询它很多次，它成为你生活结构的一部分，不仅提高你的工作效率，也改善你的个人生活，比如推荐书籍、电影或其他你喜欢的活动。但是是的，目前的智能体还不能……你还不能把整个任务委托给它们并确信它们能完全可靠地完成整个任务。（Mike: 但一年后你认为它们会做到吗？）我认为一年后我们将开始拥有接近能做到这一点的智能体。

End of Content

章节 3：乌托邦与反乌托邦——极大丰富与生存风险

📝 本节摘要：

谈话转向 AI 发展的“最好情况”与“最坏情况”。Demis 描绘了“极大丰富（Radical Abundance）”的愿景：解决疾病、能源和资源稀缺问题，推动人类探索星际。然而，他也直言不讳地指出了恐惧：恶意行为者制造病原体、网络恐怖主义以及 AI 自身的失控（Agentic 失控）。他承认存在“非零”的灾难性风险（P(doom) > 0），因此必须投入大量资源进行防御和安全研究。

Start of Content

[原文] [Mike Allen]: and bullcase barecase what is the best case for what AI can do for the world and what do you fear most

[译文] [Mike Allen]: 那么说说看好的一面和看坏的一面（bull case/bear case），AI 能为世界做的最好的事情是什么？你最恐惧的又是什么？

[原文] [Demis Hassabis]: well look the the the the the best case scenario that that I've always dreamed about and why I've worked my whole life on on on AI and you know getting closer to this moment we've been working towards for decades now many of us is um uh a kind of I somes call it radical abundance so this idea we solved a lot of the biggest issues confronting uh society and humanity today so whether that's free uh uh renewable clean energy maybe we sold fusion or better battery optimal batteries and and solar uh materials semiconductors you know material science we've solved a lot of diseases so then we're in a situation where you know we're in this new era post scarcity era and we're potentially you know humanity's is is flourishing and traveling to the stars and spreading consciousness to the to the galaxy

[译文] [Demis Hassabis]: 嗯，我一直梦想的最佳情景——也是我毕生致力于 AI 事业、并且我们许多人为之努力了几十年终于接近这一刻的原因——是一种我有时称之为“极大丰富（Radical Abundance）”的状态。这就是说我们解决了当今社会和人类面临的许多重大问题，无论是免费的可再生清洁能源——也许我们解决了核聚变，或者优化了电池和太阳能材料、半导体等材料科学问题——还是我们治愈了许多疾病。那样我们就处于一种新时代，一个“后稀缺时代”，人类可能会蓬勃发展，星际旅行，并将意识传播到银河系。

[原文] [Mike Allen]: and what do you fear most

[译文] [Mike Allen]: 那你最恐惧的是什么？

[原文] [Demis Hassabis]: well even that utopian kind of view has some questions around it about what will be um our purpose as humans if there are these technologies and that are out there that are solving all these problems all be left to solve you know I worry about that as a scientist and you know the scientific method even so there's that but there's also obviously the the well-known uh down challenges and risks with AI of well twofold one is bad actors um uh using AI for harmful ends um or the AI itself as it gets closer to AGI and becomes more gentic um it goes off the rails in some way that harms humanity

[译文] [Demis Hassabis]: 即便在那样的乌托邦愿景中，也存在一些问题，比如如果这些技术解决了所有问题，既然没有什么问题留给我们去解决，那么我们作为人类的目标是什么？作为一名科学家，甚至考虑到科学方法本身，我对此感到担忧。但除此之外，显然还有众所周知的 AI 挑战和风险，主要分为两方面：一是恶意行为者利用 AI 达到有害目的；二是 AI 本身，随着它接近 AGI 并变得更具自主代理性（agentic），它可能会以某种危害人类的方式“脱轨”。

[原文] [Mike Allen]: so you mentioned going off the rails um how worried are you about these catastrophic outcomes your level of concern i'm just going to rattle them off one pathogens created by an evil actor using AI mhm i think that's definitely one of the one of the bad use case scenarios that we have to guard against for sure energy or water cyber terror using AI by a foreign actor

[译文] [Mike Allen]: 你提到了“脱轨”。你对这些灾难性后果有多担心？你的担忧程度如何？我快速列举几个：第一，邪恶行为者利用 AI 制造病原体。（Demis: 嗯，我认为那绝对是我们必须防范的糟糕应用场景之一。）第二，外国行为者利用 AI 对能源或水资源进行网络恐怖袭击。

[原文] [Demis Hassabis]: yeah that that's probably almost already happening now I would say maybe not with very sophisticated AI yet but I think that's the most obvious vulnerable vector um and which is why we focus quite a lot and we are focusing quite a lot as Google and as DeepMind on on AI for cyber security so so to power up the defensive side of that equation

[译文] [Demis Hassabis]: 是的，我想说这种情况可能几乎已经发生了，也许还没用上非常复杂的 AI，但这可能是最明显的脆弱环节。这也是为什么我们在 Google 和 DeepMind 非常关注“用于网络安全的 AI”，旨在增强防御方的力量。

[原文] [Mike Allen]: AI operating outside human control on its own

[译文] [Mike Allen]: 第三，AI 在人类控制之外自行运作。

[原文] [Demis Hassabis]: well this goes back to the agentic stuff where I think as that becomes more sophisticated and it's clear why the industry will build those things because they'll be more useful as things like assistance um so they're definitely going to happen but the more aentic and autonomous they are the more room there is for these things to uh deviate from what you maybe had intended when you gave the initial instruction or the initial goal so this is a very active area of research which is to how to make sure that systems that maybe are capable of continual learning or online learning stay uh within the guard rails that that you set

[译文] [Demis Hassabis]: 这回到了前面说的自主代理（agentic）的话题。随着这些系统变得更复杂——行业显然会构建这些东西，因为作为助手它们会更有用，所以这肯定会发生——但它们越具有自主性和独立性，它们偏离你初始指令或目标的空间就越大。所以这是一个非常活跃的研究领域：如何确保那些可能具备持续学习或在线学习能力的系统，始终保持在你设定的护栏之内。

[原文] [Demis Hassabis]: i mean I think the good news is um because AI is become such so big commercially and for enterprises if you think about renting or selling one of your agents as a model provider leading model provider to another big business those businesses will want guarantees around the agents behavior what it does with their data what it does with their the customers and if those things go wrong they're not going to be existential in any way but you'll lose the business for sure so because why would that business enterprise go with that provider they would choose a different provider that was more responsible and had better guarantees so I think what's great about that is um that that will it will sort of capitalism will reward sort of naturally uh ideally more responsible actors

[译文] [Demis Hassabis]: 我认为好消息是，由于 AI 在商业和企业应用上变得如此巨大，试想作为领先的模型提供商，你要把智能体出租或出售给另一家大企业，那些企业会要求对智能体的行为、数据处理方式以及与客户的互动方式提供担保。如果这些出了问题，虽然不至于造成生存危机，但你肯定会失去生意。因为企业为什么还要选那个供应商呢？他们会选择另一个更负责任、担保更好的供应商。所以我认为好的一点是，资本主义会自然地奖励那些更负责任的参与者。

[原文] [Mike Allen]: but it's possible that the AI could jump the moat jump the guard rail potentially if done wrong i mean it's there was always a possibility we're we nobody really knows what the um that's one of the big unknowns i think it's non zero that potential uh so it's worth very seriously considering and mitigating against but um you know I hear people talk you know give very precise percentages about what the chances of these poom a p doom which I think is kind of nonsense because no one knows what it is what I know is it's so you don't you don't quantify it but you say it's it's non zero so clearly if your PDM is non zero then you you you know you must put significant resources and and and attention on that

[译文] [Mike Allen]: 但如果做错了，AI 还是有可能跃过护城河、跳出护栏的吧？（Demis: 我的意思是，总是有这种可能性的。没人真正知道……这是巨大的未知数之一。）我认为这种可能性是非零（non-zero）的，所以值得非常严肃地考虑并加以防范。但我听到人们给出非常精确的百分比来描述这种可能性——所谓的 P(doom)（毁灭概率）——我认为那有点胡扯，因为没人知道确切数字。但我知道的是……（Mike: 所以你不量化它，但你说它是非零的？）是的，显然如果你的毁灭概率是非零的，你就必须投入大量资源和注意力在上面。

End of Content

章节 4：中美 AI 竞赛与被低估的“视频理解”能力

📝 本节摘要：

关于地缘政治，Demis 认为西方在算法创新上仍领先中国，尽管中国在快速追随且拥有强大的工程团队。他指出，撇开芯片限制不谈，中国目前尚未展示出超越现有最先进水平（SOTA）的原创算法突破。随后，Demis 提到了目前被大众最严重低估的 AI 能力：多模态视频理解。他举了电影《搏击俱乐部》的例子，说明 Gemini 能够理解视频中的深层概念和象征意义，而不仅仅是像素。

Start of Content

[原文] [Mike Allen]: where is the US winning the AI race against China and where are we losing

[译文] [Mike Allen]: 在与中国的 AI 竞赛中，美国在哪里赢了，又在哪里输了？

[原文] [Demis Hassabis]: um I I I think that we're still in the in the US and in the west um in the lead uh if you look at the at the latest benchmarks and um the latest systems but they're not you know China is not far behind if you look at the latest DeepSseek or the latest smallers they're very good and they there are some very capable teams there so maybe we're you know the lead is only a matter of months as opposed to years at this point

[译文] [Demis Hassabis]: 嗯，我认为美国和西方仍然处于领先地位。如果你看最新的基准测试和系统，确实如此。但中国并未落后太远。如果你看最新的 DeepSeek 或其他较小的模型，它们非常好，那里有一些非常有能力的团队。所以目前的领先优势可能只是几个月的问题，而不是几年。

[原文] [Mike Allen]: because when you put chips aside AI China probably is winning

[译文] [Mike Allen]: 因为如果撇开芯片不谈，中国在 AI 上可能正在赢？

[原文] [Demis Hassabis]: um no I think chips is one thing but I think algorithmically innovation wise I think the West still has the edge so I don't think any of the Chinese models or or companies have shown they can innovate on algorithmically something new that um beyond the state-of-the-art they they they've been very good at um uh fast sort of following the the current uh state-of-the-art

[译文] [Demis Hassabis]: 不，我认为芯片是一方面，但在算法创新方面，我认为西方仍然拥有优势。我认为中国的模型或公司还没有展示出能够在算法上创新出超越当前最先进水平（state-of-the-art）的新东西。他们非常擅长快速跟随当前的最先进水平。

[原文] [Mike Allen]: our last zoom out question and you're going to like this one what's the most astonishing thing about AI that you think gets shockingly little attention

[译文] [Mike Allen]: 我们“宏观视角”的最后一个问题，你会喜欢这个的：你认为 AI 最令人震惊、却鲜有人关注的事情是什么？

[原文] [Demis Hassabis]: the most astonishing thing about AI that gets shocking little little attention wow yeah i think if I think of the things we're working on and already have working it's the um multimodal understanding these models have like if you and multimodal video yes video uh image and and I mean audio but I'm thinking specifically video actually so if you if you give Gemini a YouTube video to process you can ask it all sorts of incredible things about the video that it's just sort of mind-blowing to me that it can understand sort of conceptually in a lot of cases like not always but in many really impressive cases what's happening

[译文] [Demis Hassabis]: 最令人震惊却鲜受关注的事……哇。如果我想想我们正在做且已经奏效的事情，那是这些模型拥有的多模态理解能力。特别是视频。（Mike: 多模态视频？）是的，视频、图像，还有音频，但我这里特指视频。如果你给 Gemini 一个 YouTube 视频去处理，你可以问它关于视频的各种不可思议的问题。让我感到震撼的是，它能在很多情况下——虽然不是总是，但在很多令人印象深刻的案例中——从概念上理解正在发生什么。

[原文] [Mike Allen]: can understand example of a question

[译文] [Mike Allen]: 能理解？举个问题的例子。

[原文] [Demis Hassabis]: um well I've asked questions on on like um you know one of I mean look this was just something I tested Gemini on the other day was was um I love the film Fight Club and uh there's some scene in it I think where Brad Pitt or or or maybe it's Ed Norton I can't remember takes off his ring uh uh before having a fight and the sort of um I asked you know Gemini like what's the significance of of of of that of that action and you know he came up with a very interesting sort of philosophical point about leaving behind uh everyday life and and just sort of symbolically showing that um was you know very interesting kind of meta insight that that you know these systems have now and I think if you use it the other thing that's sort of not appreciated is like we have this thing called Gemini Live where you can point your phone at something and say you're a mechanic uh uh it can actually just help you with whatever you know task you have in front of you ideally that should be glasses because you want to have your hands free really for that um but I think people don't realize how um how powerful that multimodality capability is yet

[译文] [Demis Hassabis]: 嗯，比如我前几天测试 Gemini 的时候问了一个问题。我很喜欢电影《搏击俱乐部》，里面有一场戏，我不记得是布拉德·皮特还是爱德华·诺顿，在打架前摘下了戒指。我问 Gemini：“这个动作的意义是什么？”它给出了一个非常有趣的哲学观点，关于抛开日常生活，以及这象征性地展示了……这是一种非常有趣的“元洞察（meta insight）”，这些系统现在已经具备了。另外一个未被充分赏识的功能是 Gemini Live，你可以用手机对着某样东西，说“你是个机械师”，它实际上可以帮助你完成眼前的任务。理想情况下这应该用在眼镜上，因为你需要腾出双手，但我认为人们还没有意识到这种多模态能力有多强大。

End of Content

章节 5：深入 Gemini 3 与 转向大语言模型的决策

📝 本节摘要：

Mike Allen 将话题转入“微观视角（Zoom In）”，聚焦 DeepMind 最新发布的 Gemini 3。Demis 强调了该模型在“细微差别（Nuance）”上的突破，包括它有个性、会温和反驳用户、以及能一次性生成游戏代码（One-shotting games）。此外，Demis 回顾了当初在拥有多个研究方向（如 AlphaZero）时，为何依据“科学方法”和实证证据，决定将核心资源转向大语言模型（LLMs）的战略决策过程。

Start of Content

[原文] [Mike Allen]: all right you've given us the perfect bridge in transition to zooming in uh congratulations on Gemini 3 last month uh your gamechanging uh model you say it reasons with unprecedented depth and nuance tell us what's unique about the nuance part of Gemini 3

[译文] [Mike Allen]: 好的，你给了我们要转入“微观视角”的完美过渡。祝贺你们上个月发布了 Gemini 3，你们那个改变游戏规则的模型。你说它以其前所未有的深度和细微差别进行推理。告诉我们 Gemini 3 在“细微差别（nuance）”方面有什么独特之处。

[原文] [Demis Hassabis]: yeah I think it's just um uh uh we're really pleased with the the the the almost the personality of it the style of it as well as its capability i I I I like the way um that it answers succinctly it pushes back a little bit if you're doesn't just agree with whatever you're saying it pushes back gently on some ideas that if they're not if they don't make sense and I think people are appreciating uh it seems you know sort of I feel like it's a you can feel it's a bit of a step change in its kind of intelligence and therefore usefulness

[译文] [Demis Hassabis]: 是的，我们对它的个性、风格以及能力都非常满意。我喜欢它回答简洁的方式，如果你说的不合理，它不会只是一味附和，而是会温和地反驳。我认为人们正在欣赏这一点。你能感觉到它的智能、以及随之而来的实用性都有了阶跃式的变化。

[原文] [Mike Allen]: and what's something that Gemini has answered or produced where you said I didn't know it could do that or I didn't know it would do that

[译文] [Mike Allen]: 有没有 Gemini 回答或生成的某个东西，让你惊叹“我不知道它能做这个”或者“我不知道它会这么做”？

[原文] [Demis Hassabis]: well actually this is the the amazing thing of when you why we love what what we're doing so much is that the this era we're now in with research connected to product the great thing about that is that you get millions and potentially at Google billions of users immediately take advantage of the new technology you put out there and uh we're continually surprised by the cool things that people figure out very quickly um to use these models for um and a lot of those things sort of you know tend to go viral but the thing I I most enjoyed with Gemini 3 was oneshotting uh games so back to my very first career of making AI for games I think we're very close now with these models maybe the next version models where you could start really creating perhaps commercial grade games uh you know vibe coding them uh with you know in a few hours which used to take years and that shows nuance what does that show about the model

[译文] [Demis Hassabis]: 这正是我们如此热爱目前工作的神奇之处。在这个研究与产品紧密相连的时代，最棒的是你能立即让数百万甚至在 Google 可能是数十亿用户使用你发布的新技术。我们不断惊讶于人们能如此迅速地发现这些模型的酷炫用法，很多用法甚至会像病毒一样传播。但我最喜欢 Gemini 3 的一点是它能一次性生成游戏（one-shotting games）。回到我最初制作游戏 AI 的职业生涯，我认为现在的模型，或者下一代模型，已经非常接近能让你真正开始创造商业级游戏了。你知道，通过“凭感觉编程（vibe coding）”，在几小时内完成过去需要几年才能做完的事。（Mike: 这显示了细微差别，这说明了模型的什么特点？）

[原文] [Demis Hassabis]: well I think it's just incredible uh uh depth and and and capability of these models to understand very high level instructions and and produce you know very detailed outputs and the other things that uh uh Gemini 3 particularly is good at is front-end work and developing you know websites and it's it's pretty good aesthetically and creatively as well as um technically

[译文] [Demis Hassabis]: 这展示了这些模型拥有不可思议的深度和能力，能理解非常高层的指令并生成非常详细的输出。Gemini 3 特别擅长的另一件事是前端工作和网站开发，它在美学和创意上与技术上一样出色。

[原文] [Mike Allen]: and one more question on Gemini 3 a little back story and you had a number of irons in the fire but LLM's the textbased uh large language models uh you didn't necessarily go all in on that as the holy grail something that Walter Isacson the great author and thinker and your friend said to me is that when you saw the power of the LLM you did a pivot a pureette as Walter said it and were able to leapfrog to great success and Walter's point was that most business people would have been stubborn might have doubled triple down on their other bets how did you make this decision to go allin on your LLM

[译文] [Mike Allen]: 关于 Gemini 3 再问一个幕后故事。你当时有多个项目在同时进行，但对于 LLM（基于文本的大语言模型），你并没有一开始就把它视为“圣杯”全力投入。伟大的作家、思想家、也是你的朋友沃尔特·艾萨克森（Walter Isaacson）曾对我说，当你看到 LLM 的力量时，你做了一个转型——沃尔特称之为“华丽转身（pirouette）”——并成功实现了跨越式发展。沃尔特的观点是，大多数商人可能会固执己见，会在其他赌注上加倍、甚至三倍下注。你是如何决定全力投入 LLM 的？

[原文] [Demis Hassabis]: well I think this is again the the the beauty of and the strength of the scientific method if you're a true scientist you can't get too dogmatic about some idea you have you you need to go with where the empirical evidence is taking you so first of all this is this is Walter is probably referring back to the 2017 2018 era so there we had a lot of irons in the fire as we said we had our own very capable language models they were called Chinchilla and then Sparrow and we had these various different code names for them um they weren't publicly released but they were internal in fact some of the scaling laws were originally figured out by our team they're called the Chinchilla scaling laws um but we also had other types of programs alpha zero things that were building on Alpha Go pure RL systems and we also had some cognitive science more neuroscience inspired architectures as well and at the time all we weren't sure my job is to make sure we build AGI uh first fast and safely right that's always been our our solve intelligence our mission at DeepMind and and so I'm kind of agnostic actually to the to the approach that's taken i'm pretty pragmatic on that that's maybe my engineering side of me is I have some theories as as a good scientist would but I'm I'm I'm at the end of the day it's got to pragmatically work and so when we started seeing the beginnings of scaling working then we increasingly put more and more resources onto that branch of the of the of the research tree

[译文] [Demis Hassabis]: 我认为这再次体现了科学方法的美妙和力量。如果你是一个真正的科学家，你不能对自己的某个想法过于教条，你必须跟随实证证据的指引。首先，沃尔特指的可能是 2017、2018 年那个时期。当时我们确实有很多项目在进行。我们有自己非常有能力的语言模型，叫 Chinchilla，后来还有 Sparrow，我们有各种代号。它们没有公开发布，但在内部使用。事实上，一些缩放定律（scaling laws）最早就是我们团队发现的，被称为 Chinchilla 缩放定律。但我们也有其他类型的项目，比如 AlphaZero，这是基于 AlphaGo 的纯强化学习（RL）系统；我们还有一些受认知科学和神经科学启发的架构。当时我们并不确定。我的工作是确保我们能快速且安全地率先构建 AGI，对吧？“解决智能问题”一直是我们 DeepMind 的使命。所以实际上我对采用哪种方法持不可知论态度。在这方面我很务实——这可能是我作为工程师的一面——像优秀的科学家一样，我有一些理论，但归根结底，它必须在实践中行得通。所以当我们开始看到“缩放（scaling）”开始起作用的迹象时，我们就越来越多地将资源投入到那棵研究树的分支上。

End of Content

章节 6：通往 AGI 之路、泡沫论与人才战争

📝 本节摘要：

谈及 AGI 的时间表，Demis 预测还需要 5-10 年。他认为现有的 LLM 系统在某些领域表现出色（博士级水平），但在长期规划和推理上仍有缺陷，是“参差不齐的智能”。除了扩大现有模型规模（Scaling），可能还需要一两个像 Transformer 或 AlphaGo 级别的重大突破。对于 AI 泡沫，他认为部分领域（如巨额种子轮融资）存在泡沫，但长期来看技术变革是真实的。最后，他谈到了激烈的人才争夺战，认为 DeepMind 凭借使命感和全栈资源仍具吸引力。

Start of Content

[原文] [Mike Allen]: something that's refreshing about your approach is with artificial general intelligence human capable uh AI you don't shy away from it some other people say "Well we won't know or we're already there or it doesn't matter." You say that it does matter and we will know and you say it's not far off yeah we're definitely not there now so and and I and actually quite close is how you say yes quite close i think we're like five to 10 years away if you were to ask me

[译文] [Mike Allen]: 你的态度令人耳目一新的是，对于通用人工智能（AGI）——即具备人类能力的 AI——你并不回避。有些人说“我们不知道什么时候到”或者“我们已经到了”或者“这不重要”。你说这很重要，我们会知道什么时候到达，而且你说它并不遥远。（Demis: 是的，我们现在肯定还没到。）你说其实很近了？（Demis: 是的，很近。）如果你问我的话，我认为大约还有 5 到 10 年。

[原文] [Demis Hassabis]: i think Say that again five to 10 years away i think my bar though is quite high so this is the the we define AGI as you know the a system that that exhibits all the cognitive capabilities we have and that includes uh inventive and creative capabilities i think there are missing there's as all of you have used the current LLMs there are they're they're amazing in some ways they're really impressive in some senses in some they've got incredible almost PhD levels uh key skills in some areas IMO gold medals and so on but in other areas they're very flawed still and so they're these sort of jagged intelligences so the you would expect across the board consistency from a true AGI and they're missing other capabilities like continual learning online learning long-term planning and reasoning they can't do any of these things currently I think they will be able to but maybe one or two more breakthroughs are going to be required

[译文] [Demis Hassabis]: 我认为是 5 到 10 年。不过我的标准设得很高。我们将 AGI 定义为展示出人类拥有的所有认知能力的系统，这包括发明和创造能力。我想目前还缺一些东西。大家都用过目前的 LLM，它们在某些方面很惊人，甚至在某些领域达到了博士级水平、拿到了奥数金牌等。但在其他方面它们仍然缺陷很大。所以它们是一种“参差不齐的智能（jagged intelligences）”。对于真正的 AGI，你会期望全面的稳定性。它们目前还缺少持续学习、在线学习、长期规划和推理等能力。我认为它们最终能做到，但可能还需要一两个突破。

[原文] [Mike Allen]: a question from the great Ena Frereded who we've uh seen today ... she says you're obviously um uh you've said that AI might be one advance two advances away from AGI yes will we get there just by improving LLM and generative AI or do you think that there might be a different approach that's needed to hit a GI in your 5 to 10 years

[译文] [Mike Allen]: 来自 Ina Fried 的一个问题……她说你曾表示 AI 可能距离 AGI 只有一两个进展之遥。我们是仅靠改进 LLM 和生成式 AI 就能到达那里，还是你认为在你的 5 到 10 年预测中，需要某种不同的方法才能实现 AGI？

[原文] [Demis Hassabis]: i think I think again this is an empirical question but what I do know this is this would be my best guess is um the scaling of the current systems you we must push that to the maximum because at the minimum it will be a key component of the final AGI system it could be the entirety of the AGI system there's a chance that just scaling will get you there but my guess is if I was to guess from where I my vantage point now is that one or two more big breakthroughs when I mean there's innovation going on all the time by the way even including in scaling um existing techniques but I'm talking like a transformer level or alpho level type of breakthrough i think we might I suspect when we look back in once AGI is done that one or two of those things were still required in addition to scaling

[译文] [Demis Hassabis]: 我认为这依然是一个实证问题。但我知道——或者说我最好的猜测是——我们必须将当前系统的缩放（scaling）推向极致，因为这至少会是最终 AGI 系统的关键组成部分，甚至可能是全部。仅靠缩放有可能就能到达那里，但我现在的猜测是，还需要一两个大的突破。我指的不是一直在进行的渐进式创新，而是像 Transformer 或 AlphaGo 级别的那种突破。我怀疑当我们建成 AGI 后回顾时，会发现除了缩放之外，还需要那样一两样东西。

[原文] [Mike Allen]: another question from uh Ena you obviously are a big believer in AI but if you look at what's being spent that doesn't mean that there might not be a big enough bubble to rattle the economy how worried are you about that

[译文] [Mike Allen]: Ina 的另一个问题：你显然笃信 AI，但看看现在的投入金额，这不代表不会出现一个足以震动经济的巨大泡沫。你对此有多担心？

[原文] [Demis Hassabis]: um I think we there I think it's not a binary i think some parts of the AI industry are probably in a bubble like you know I don't know like the seed rounds of you know you know $50 billion seed rounds and things like that seems a little bit unsustainable but um on the other hand of course I more than anyone believes that AI is the most transformative uh technology ever so I think in the fullness of time this is all going to be uh more than justified

[译文] [Demis Hassabis]: 嗯，我认为这不是非黑即白的。我认为 AI 行业的某些部分可能处于泡沫中，比如那些……你知道，500亿美元估值的种子轮融资之类的事情似乎有点不可持续。但另一方面，我比任何人都坚信 AI 是有史以来最具变革性的技术，所以我认为从长远来看，这一切投入都将被证明是物超所值的。

[原文] [Mike Allen]: the AI recruiting wars what's the end state of this competition for talent

[译文] [Mike Allen]: 关于 AI 招聘战，这场人才竞争的终局是什么？

[原文] [Demis Hassabis]: well look it's gone pretty crazy recently things like what Meta have been doing and you know everyone's got to do what what makes sense for them uh what we found for us is that we want people who are missiondriven we have I think the best mission we have the full stack so I think if you want to do the most impactful work and have the most positive impact on the world then I think there's nowhere better uh than than at Google DeepMind

[译文] [Demis Hassabis]: 嗯，最近确实变得很疯狂，比如 Meta 所做的事情。每个人都要做对自己合理的事。我们发现，我们需要的是那些使命驱动的人。我认为我们拥有最好的使命，我们拥有全栈资源。所以如果你想做最具影响力的工作，对世界产生最积极的影响，我认为没有比 Google DeepMind 更好的地方了。

End of Content

章节 7：哲学思考——游戏、人类适应力与足球

📝 本节摘要：

访谈最后进入哲学与轻松话题。Demis 回答了关于“AI 产生自我意识/自私利益”的风险。他随后阐述了游戏对他思维的影响：游戏是现实世界的缩影，是训练决策能力的最佳场所。对于人类能否适应 AI 带来的剧变，他持乐观态度，以人类从狩猎采集者进化到现代文明为例，证明人类大脑具有无限的适应性。最后，作为利物浦死忠粉，他简短讨论了 AI 在体育（如世界杯）中的应用前景。

Start of Content

[原文] [Mike Allen]: this is a question from James Vanderhigh an entrepreneurial young mind at High Point University in North Carolina he says "There's a lot of conversation about AI gaining a mind of its own is there a scenario where AI could act in its selfinterest?"

[译文] [Mike Allen]: 这是一个来自北卡罗来纳州海波因特大学的年轻创业者 James VandeHei 的问题。他说：“有很多关于 AI 产生自我意识的讨论，是否存在 AI 会为了自身利益而行动的情景？”

[原文] [Demis Hassabis]: Well that's a great question and and it's related to the some of the the the the more sort of catastrophic outcomes is if that went wrong that would be one of the issues that with agentbased systems or very autonomous systems if somehow they developed a self-interest that was some in some sense sense conflicting with what the designers or even perhaps humanity wanted it to do

[译文] [Demis Hassabis]: 这是一个很好的问题，它与那些更具灾难性的后果有关。如果出问题，这会是基于智能体的系统或高度自主系统的问题之一：如果它们在某种程度上发展出了与设计者甚至人类意愿相冲突的“自身利益”。

[原文] [Mike Allen]: and finishing with a fun thing you're still a gamer what does gaming teach us about the world and what does gaming teach us about where these machines are headed

[译文] [Mike Allen]: 最后聊点有趣的。你仍然是个玩家，游戏教会了我们关于世界的什么？关于这些机器的未来，游戏又教会了我们什么？

[原文] [Demis Hassabis]: well look I think uh my chess background and and my training in that and then other games subsequently has been critical to how I do my work and both in business and in science uh I think the thing I love about games and there's many things I've loved about them but I love the creativity of making them but I also just playing them I think is the best way to train your mind because the best games whether that's chess or go or whatever or poker they're microcosms of something in the real world right but in general you don't get in the real world to have several practice goes at making the decision correctly in that moment maybe in the real life you only get a dozen of those critical moments but you can practice your decision-m capabilities as much as you want uh w within the the the the almost the simulation really of the world with games um and as long as you take the games very seriously so you put you put a lot of thought into your decision-m it really does train your your decision-m and planning capabilities in my opinion

[译文] [Demis Hassabis]: 嗯，我的国际象棋背景及相关训练，以及后来的其他游戏，对我如何在商业和科学领域开展工作至关重要。我热爱游戏有很多原因，我喜欢制作游戏的创造力，但也喜欢玩游戏。我认为这是训练思维的最佳方式，因为最好的游戏——无论是国际象棋、围棋还是扑克——都是现实世界某种事物的缩影。但在现实世界中，你通常没有机会在关键时刻多次练习决策。也许在现实生活中你只有十几次关键时刻，但在游戏中，在这个几乎是对世界的模拟中，你可以尽情练习你的决策能力。只要你非常认真地对待游戏，在决策中投入大量思考，我认为它真的能训练你的决策和规划能力。

[原文] [Mike Allen]: now you've pointed out that our squishy brains uh evolved uh to be hunter gatherers and yet we're facing a disruption that as you put it to the Guardian will be 10 times bigger than the industrial revolution and maybe 10 times faster are we facing a situation where most humans can't keep up and maybe no human including you can keep up

[译文] [Mike Allen]: 你曾指出，我们这柔软的大脑是为了做狩猎采集者而进化的，但我们正面临一场颠覆——正如你对《卫报》所说——其规模将是工业革命的10倍，速度可能是10倍。我们是否面临着大多数人类无法跟上，甚至包括你在内没人能跟上的局面？

[原文] [Demis Hassabis]: well the good news is and I think my point on the hunt gather was look how adaptive our brains have been we we evolved to be hunter gatherers and yet here we are sitting in our modern cities modern civilization with all the technology around us and um you know the same human brain pretty much has been able to adapt to that so I'm a really uh big believer in uh human ingenuity and um and I think we're infinitely adaptable we are the only existence proof our brains are the only existence proof of general intelligence perhaps in the known you know universe that we know of so far so we are general intelligences ourselves and so we should be able to infinitely adapt

[译文] [Demis Hassabis]: 好消息是——这也是我关于狩猎采集者那个观点的用意——看我们的大脑适应能力有多强。我们进化成狩猎采集者，但现在我们坐在现代城市、现代文明中，周围全是科技，而同样的大脑几乎适应了这一切。所以我非常相信人类的智慧，我认为我们具有无限的适应能力。我们的大脑是已知宇宙中唯一的“通用智能”存在的证明。我们自己就是通用智能，所以我们应该能够无限适应。

[原文] [Mike Allen]: and as we say goodbye you're a lifelong Liverpool fan you've helped them with their analytics how will AI affect and inform the World Cup here in North America

[译文] [Mike Allen]: 在我们道别之际，你是个终身利物浦球迷，你也帮他们做过数据分析。AI 将如何影响和指导在北美举办的世界杯？

[原文] [Demis Hassabis]: well we've had a lot of we've had a lot of teams approach us for for help too and um and I have to be try and be equal with that but it's hard having a lifelong spot of Liverpool but I'm looking forward to trying to make it out here maybe at least for the World Cup final but but let's be serious what what what will it change between now and then it's a it's a lifetime in AI between now and then right yeah well what in AI or AI for sport or just in Yes yeah well I mean look sport has immense amount of data and it's all about uh extreme elite performance so it's actually a natural bed fellow for for AI to to come in and and help optimize that process even further and without giving away a trade secret what will it be able to do for a World Cup team uh maybe score more headers from from corners you know if you place the that's one of the things I think our system found out like precise positioning of the players deus thanks for making a

[译文] [Demis Hassabis]: 嗯，已经有很多球队找我们寻求帮助了。我必须尽量一视同仁，虽然作为利物浦的终身支持者这很难。我很期待能来这里，也许至少来看世界杯决赛。但严肃地说，从现在到那时会改变什么？在 AI 领域，这段时间就像过了一辈子，对吧？（Mike: 是的，AI 在体育方面会有什么变化？）体育拥有海量数据，而且关乎极致的精英表现，所以它实际上是 AI 介入并帮助进一步优化流程的天然伙伴。如果不泄露商业机密的话，它能为世界杯球队做什么？也许是在角球中进更多头球。你知道，如果你能精确安排球员的位置——这是我们系统发现的事情之一。Demis，感谢你的参与。

End of Content